class_docstr: Compute the Pan Matrix Profile with a distributed dask cluster
  
  This is based on the SKIMP algorithm.
  
  Parameters
  ----------
  client : client
      A Dask or Ray Distributed client. Setting up a distributed cluster is beyond
      the scope of this library. Please refer to the Dask or Ray Distributed
      documentation.
  
  T : numpy.ndarray
      The time series or sequence for which to compute the pan matrix profile
  
  m_start : int, default 3
      The starting (or minimum) subsequence window size for which a matrix profile
      may be computed
  
  m_stop : int, default None
      The stopping (or maximum) subsequence window size for which a matrix profile
      may be computed. When `m_stop = Non`, this is set to the maximum allowable
      subsequence window size
  
  m_step : int, default 1
      The step between subsequence window sizes
  
  p : float, default 2.0
      The p-norm to apply for computing the Minkowski distance. Minkowski distance is
      typically used with `p` being 1 or 2, which correspond to the Manhattan distance
      and the Euclidean distance, respectively.
  
  Attributes
  ----------
  PAN_ : numpy.ndarray
      The transformed (i.e., normalized, contrasted, binarized, and repeated)
      pan matrix profile
  
  M_ : numpy.ndarray
      The full list of (breadth first search (level) ordered) subsequence window
      sizes
  
  Methods
  -------
  update():
      Compute the next matrix profile using the next available (breadth-first-search
      (level) ordered) subsequence window size and update the pan matrix profile
  
  Notes
  -----
  `DOI: 10.1109/ICBK.2019.00031     <https://www.cs.ucr.edu/~eamonn/PAN_SKIMP%20%28Matrix%20Profile%20XX%29.pdf>`__
  
  See Table 2
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docstr: Initialize the `stimp` object and compute the Pan Matrix Profile
  
  Parameters
  ----------
  client : client
      A Dask or Ray Distributed client. Setting up a distributed cluster is beyond
      the scope of this library. Please refer to the Dask or Ray Distributed
      documentation.
  
  T : numpy.ndarray
      The time series or sequence for which to compute the pan matrix profile
  
  min_m : int, default 3
      The minimum subsequence window size to consider computing a matrix profile
      for
  
  max_m : int, default None
      The maximum subsequence window size to consider computing a matrix profile
      for. When `max_m = None`, this is set to the maximum allowable subsequence
      window size
  
  step : int, default 1
      The step between subsequence window sizes
  
  p : float, default 2.0
      The p-norm to apply for computing the Minkowski distance. Minkowski distance
      is typically used with `p` being 1 or 2, which correspond to the Manhattan
      distance and the Euclidean distance, respectively.
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code: def __init__(
      self,
      client,
      T,
      min_m=3,
      max_m=None,
      step=1,
      p=2.0,
  ):
      """
      Initialize the `stimp` object and compute the Pan Matrix Profile
  
      Parameters
      ----------
      client : client
          A Dask or Ray Distributed client. Setting up a distributed cluster is beyond
          the scope of this library. Please refer to the Dask or Ray Distributed
          documentation.
  
      T : numpy.ndarray
          The time series or sequence for which to compute the pan matrix profile
  
      min_m : int, default 3
          The minimum subsequence window size to consider computing a matrix profile
          for
  
      max_m : int, default None
          The maximum subsequence window size to consider computing a matrix profile
          for. When `max_m = None`, this is set to the maximum allowable subsequence
          window size
  
      step : int, default 1
          The step between subsequence window sizes
  
      p : float, default 2.0
          The p-norm to apply for computing the Minkowski distance. Minkowski distance
          is typically used with `p` being 1 or 2, which correspond to the Manhattan
          distance and the Euclidean distance, respectively.
      """
      super().__init__(
          T,
          min_m=min_m,
          max_m=max_m,
          step=step,
          percentage=1.0,
          pre_scraamp=False,
          client=client,
          mp_func=aamped,
          p=2.0,
      )
  
file_path: stumpy/stumpy/aamp_stimp.py
imports: import numpy
  from .None import core
  from .aamp import aamp
  from .aamped import aamped
  from .scraamp import scraamp
label_desc: Use image node_id 1 to create a new aamp_stimped object from inherited base classes: _aamp_stimp with example: obj = aamp_stimped(client, T, min_m, max_m, step, p)